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. Leveraging ODMDwC's ability to find and track linear approximation of a non-linear system while incorporating control effects%2C the proposed method dynamically adapts to its changing behavior due to aging and seasonality. This approach enables the detection of changes in spatial%2C temporal%2C and spectral patterns%2C providing a robust solution that preserves correspondence between the score and the extent of change in the system dynamics. We formulate a truncated version of ODMDwC and utilize higher-order time-delay embeddings to mitigate noise and extract broad-band features. Our method addresses the challenges faced in industrial settings where safety-critical systems generate non-uniform data streams while requiring timely and accurate change-point detection to protect profit and life. Our results demonstrate that this method yields intuitive and improved detection results compared to the Singular-Value-Decomposition-based method. We validate our approach using synthetic and real-world data%2C showing its competitiveness to other approaches on complex systems' benchmark datasets. Provided guidelines for hyperparameters selection enhance our method's practical applicability.?quality=80&w=800)
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